Prediction of ADRs using NMF and Weighted NMFs
Arshad Ahamed Shajahan, Sai Shasank Dandu
1 School of Mathematical and Statistical Sciences, University of Galway
Background and Objectives
Adverse drug reactions (ADRs) are a common problem in clinical and pharmacovigilance research and can lead to serious patient harm and biased conclusions if modelled poorly. Sparse, noisy, and highly imbalanced drug ADR data often cause standard machine learning methods to perform no better than naïve frequency‑based approaches, unless appropriate low‑rank and kernel‑based methods are used with clear assumptions [1].
Objectives
- Explain the ADR profile prediction problem and its statistical challenges in imbalanced, noisy health data.
- Explore low‑rank and kernel‑based methods (e.g. NMF+kernel regression and alternatives) for ADR prediction.
Datasets
The primary source of datasets are DGIdb 4.0, SIDER 4.1 and PubChem database.
=======Data Sources and Descriptive Statistics of Datasets
The primary source of data are DGIdb 4.0, SIDER 4.1 and PubChem database.
>>>>>>> d63ef35872443e689156e15c694db1783f55380d- Drug-gene interaction pair: Intersection of drugs from DGIdb 4.0 and SIDER 4.1, generated to the binary matrix form.
- Chemical fingerprints: Data from PubChem database, generated to the binary matrix form.
- Drug & side effects: Drug along with it’s side effects are extracted from SIDER 4.1 database.
Descriptive Statistics
The above graph mentions the drugs majority of the drugs have more than 200 side effects, emphasising the importance of this study.
From the dataset, these are the drugs which possess the most side effects displayed alongside the most common side effects.
The above graph mentions the drugs that has the most side effects.
The above graph depicts the side effect that was most common among the drugs.
Early Results / Descriptive Statistics of Datasets
Usually you want to have a nice table displaying some important results that you have calculated. In posterdown this is as easy as using the kable table formatting you are probably use to as per typical R Markdown formatting.
You can reference tables like so: Table 1. Some basic summaries of the dataset are below:
| Sepal.Length | Sepal.Width | Petal.Length | Petal.Width |
|---|---|---|---|
| 5.1 | 3.5 | 1.4 | 0.2 |
| 4.9 | 3.0 | 1.4 | 0.2 |
| 4.7 | 3.2 | 1.3 | 0.2 |
| 4.6 | 3.1 | 1.5 | 0.2 |
| 5.0 | 3.6 | 1.4 | 0.2 |
| 5.4 | 3.9 | 1.7 | 0.4 |
| 4.6 | 3.4 | 1.4 | 0.3 |
| 5.0 | 3.4 | 1.5 | 0.2 |
| 4.4 | 2.9 | 1.4 | 0.2 |
| 4.9 | 3.1 | 1.5 | 0.1 |
Figure 1, and Figure ?? below show the patterns in our dataset. Make sure that all the details in your plots will be legible when printed (legend text, axis text, and any labels)
Figure 1: Early Performance of ADR Prediction Methods
>>>>>>> d63ef35872443e689156e15c694db1783f55380dMethods and Early Results
1.We implemented five ADR profile prediction methods using DGI features only: Naïve frequency model (ADR prevalence per column), kernel regression (KR), linear SVM, RBF‑kernel SVM, and VKR (NMF + kernel ridge regression).
Preliminary analysis on the DGI dataset shows that the Naïve baseline and VKR achieve the highest AUROC (≈0.91), while KR and VKR achieve the best AUPR (≈0.41–0.42), clearly outperforming SVM variants on both metrics. VKR therefore provides the best overall trade‑off between discrimination (AUROC) and rare ADR detection (AUPR), motivating its use as the main reference method in further experiments.
>>>>>>> d63ef35872443e689156e15c694db1783f55380d
Next Project Steps
We plan to conduct further analysis using:
- Extended Data set with latest drug available from SIDER 4.1 & DGIdb database 1
- New methods using weighted NMF, SVD and weighted SVD 2.
We will use the PYTHON Programming for this.
GitHub
The code and datasets for this project can be viewed at our GitHub repository here: https://github.com/arshad4387/ADR-Prediction.git
<<<<<<< HEADAcknowldgement
We would like to thank Dr. Yezhao and Dr. Haixuan for their and sharing the code and data through the github page.
======= >>>>>>> d63ef35872443e689156e15c694db1783f55380dReferences
[1] Zhong, Y., Seoighe, C., & Yang, H. (2024). Non-Negative matrix factorization combined with kernel regression for the prediction of adverse drug reaction profiles. Bioinformatics Advances, 4(1), vbae009.